{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c999498a-1802-4d3c-ad22-6ee1f89428d2",
   "metadata": {},
   "source": [
    "<font size=5>数据说明</font>\n",
    "\n",
    "![](data_describe.png)"
   ]
  },
  {
   "attachments": {
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FAAAAAMx0l3w4oq3hnwpTu4SyZQJcRN1vRpw6lCfZnAVV+KIxKHShNbY+OflO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c9KmYcjvmLtCzftKx6cWCgKELN7FLC9BQJYETZXkh/sondDFOxdzmG7q41sXQBREREYWXVL2Q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\n1prfr9Oelhb99Kc/Ulvzk+78K7p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    }
   },
   "cell_type": "markdown",
   "id": "0afde239-148e-47cd-af02-1bce6c2424d7",
   "metadata": {},
   "source": [
    "<font size=5>用户行为分析</font>\n",
    "\n",
    "![Behavior_analysis.png](attachment:c4a27c38-c4ef-40ab-8d6d-bcc61b1977df.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0ee057-f040-4216-a268-40f72629c55a",
   "metadata": {},
   "source": [
    "# 数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "42aa3fc6-2426-402c-9c2d-f7d546d33c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b64be918-0132-40cf-9858-82bf6d8d6acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12256906, 5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/user_action.csv')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3394280d-019f-45e2-8b02-72fd6e6ce95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98047837</td>\n",
       "      <td>232431562</td>\n",
       "      <td>1</td>\n",
       "      <td>4245</td>\n",
       "      <td>2014-12-06 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97726136</td>\n",
       "      <td>383583590</td>\n",
       "      <td>1</td>\n",
       "      <td>5894</td>\n",
       "      <td>2014-12-09 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98607707</td>\n",
       "      <td>64749712</td>\n",
       "      <td>1</td>\n",
       "      <td>2883</td>\n",
       "      <td>2014-12-18 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98662432</td>\n",
       "      <td>320593836</td>\n",
       "      <td>1</td>\n",
       "      <td>6562</td>\n",
       "      <td>2014-12-06 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    item_id  behavior_type  item_category           time\n",
       "0  98047837  232431562              1           4245  2014-12-06 02\n",
       "1  97726136  383583590              1           5894  2014-12-09 20\n",
       "2  98607707   64749712              1           2883  2014-12-18 11\n",
       "3  98662432  320593836              1           6562  2014-12-06 10\n",
       "4  98145908  290208520              1          13926  2014-12-16 21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3aa7a2b8-df42-4eb2-8db9-f37395a73608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          0\n",
       "item_id          0\n",
       "behavior_type    0\n",
       "item_category    0\n",
       "time             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看有无缺失值\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61f96996-a45d-49ef-8710-10a2d13e85c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12256906 entries, 0 to 12256905\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   user_id        int64 \n",
      " 1   item_id        int64 \n",
      " 2   behavior_type  int64 \n",
      " 3   item_category  int64 \n",
      " 4   time           object\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 467.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f27d30c-8765-4398-bfa5-6ba36b8d6a35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "整体数据的大小为 12256906\n",
      "数据集中用户数量是： 10000\n",
      "数据集中商品数量是： 2876947\n",
      "数据集中商品类别数量是： 8916\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集量级\n",
    "\n",
    "print('整体数据的大小为',data.shape[0])\n",
    "print('数据集中用户数量是：',data['user_id'].nunique())\n",
    "print('数据集中商品数量是：',data['item_id'].nunique())\n",
    "print('数据集中商品类别数量是：',data['item_category'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7088074-e5e1-48d9-9b45-78c252c7c76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将time列拆分为日期列和时间列\n",
    "\n",
    "data['date'] = data['time'].apply(lambda x: x.split(' ')[0])\n",
    "data['hour'] = data['time'].apply(lambda x: x.split(' ')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f44246e6-459d-4da5-ba2c-b71a6b0a90ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98047837</td>\n",
       "      <td>232431562</td>\n",
       "      <td>1</td>\n",
       "      <td>4245</td>\n",
       "      <td>2014-12-06 02</td>\n",
       "      <td>2014-12-06</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97726136</td>\n",
       "      <td>383583590</td>\n",
       "      <td>1</td>\n",
       "      <td>5894</td>\n",
       "      <td>2014-12-09 20</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98607707</td>\n",
       "      <td>64749712</td>\n",
       "      <td>1</td>\n",
       "      <td>2883</td>\n",
       "      <td>2014-12-18 11</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98662432</td>\n",
       "      <td>320593836</td>\n",
       "      <td>1</td>\n",
       "      <td>6562</td>\n",
       "      <td>2014-12-06 10</td>\n",
       "      <td>2014-12-06</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21</td>\n",
       "      <td>2014-12-16</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    item_id  behavior_type  item_category           time  \\\n",
       "0  98047837  232431562              1           4245  2014-12-06 02   \n",
       "1  97726136  383583590              1           5894  2014-12-09 20   \n",
       "2  98607707   64749712              1           2883  2014-12-18 11   \n",
       "3  98662432  320593836              1           6562  2014-12-06 10   \n",
       "4  98145908  290208520              1          13926  2014-12-16 21   \n",
       "\n",
       "         date hour  \n",
       "0  2014-12-06   02  \n",
       "1  2014-12-09   20  \n",
       "2  2014-12-18   11  \n",
       "3  2014-12-06   10  \n",
       "4  2014-12-16   21  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f213652-e361-4f1f-8dda-adcdae907a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id           int64\n",
       "item_id           int64\n",
       "behavior_type     int64\n",
       "item_category     int64\n",
       "time             object\n",
       "date             object\n",
       "hour             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62c60db6-1c36-4fa5-a4d0-5f9ff6924d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                  object\n",
       "item_id                  object\n",
       "behavior_type             int64\n",
       "item_category            object\n",
       "time                     object\n",
       "date             datetime64[ns]\n",
       "hour                      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  转换数据类型\n",
    "\n",
    "data['user_id'] = data['user_id'].astype('object')\n",
    "data['item_id'] = data['item_id'].astype('object')\n",
    "data['item_category'] = data['item_category'].astype('object')\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "data['hour'] = data['hour'].astype('int64')\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b90f89f1-a008-4e53-a4ba-6f5e52fd9c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-636a34292a0b>:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  data['date'].describe()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count                12256906\n",
       "unique                     31\n",
       "top       2014-12-12 00:00:00\n",
       "freq                   691712\n",
       "first     2014-11-18 00:00:00\n",
       "last      2014-12-18 00:00:00\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['date'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e760b288-5cce-4b6c-8b92-150331359959",
   "metadata": {},
   "source": [
    "# 数据分析及其可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c57a86-7fa5-45a7-bf53-ebfc4b0f06f0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 用户行为分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a9ce0c6-97d2-404f-8b73-200aee69c4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Funnel\n",
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "from pyecharts.globals import ThemeType\n",
    "\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ccdc965f-2455-4ddd-9b47-c7ace852d4ed",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[71], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m cate \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m浏览\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m收藏\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m加购物车\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m购买\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      2\u001b[0m y_data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mint\u001b[39m(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbehavior_type\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mvalues)]\n\u001b[1;32m----> 4\u001b[0m my_data \u001b[38;5;241m=\u001b[39m [[x_data[i], y_data[i]] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(x_data))]\n\u001b[0;32m      6\u001b[0m funnel \u001b[38;5;241m=\u001b[39m Funnel(init_opts\u001b[38;5;241m=\u001b[39mopts\u001b[38;5;241m.\u001b[39mInitOpts(theme\u001b[38;5;241m=\u001b[39mThemeType\u001b[38;5;241m.\u001b[39mDARK))\n\u001b[0;32m      7\u001b[0m funnel\u001b[38;5;241m.\u001b[39madd(\n\u001b[0;32m      8\u001b[0m         series_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      9\u001b[0m         data_pair\u001b[38;5;241m=\u001b[39mmy_data\n\u001b[0;32m     10\u001b[0m     )\n",
      "Cell \u001b[1;32mIn[71], line 4\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m cate \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m浏览\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m收藏\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m加购物车\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m购买\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      2\u001b[0m y_data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mint\u001b[39m(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbehavior_type\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mvalues)]\n\u001b[1;32m----> 4\u001b[0m my_data \u001b[38;5;241m=\u001b[39m [[x_data[i], \u001b[43my_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(x_data))]\n\u001b[0;32m      6\u001b[0m funnel \u001b[38;5;241m=\u001b[39m Funnel(init_opts\u001b[38;5;241m=\u001b[39mopts\u001b[38;5;241m.\u001b[39mInitOpts(theme\u001b[38;5;241m=\u001b[39mThemeType\u001b[38;5;241m.\u001b[39mDARK))\n\u001b[0;32m      7\u001b[0m funnel\u001b[38;5;241m.\u001b[39madd(\n\u001b[0;32m      8\u001b[0m         series_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      9\u001b[0m         data_pair\u001b[38;5;241m=\u001b[39mmy_data\n\u001b[0;32m     10\u001b[0m     )\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "cate = ['浏览','收藏','加购物车','购买']\n",
    "y_data = [int(i) for i in list(data['behavior_type'].value_counts().values)]\n",
    "\n",
    "my_data = [[x_data[i], y_data[i]] for i in range(len(x_data))]\n",
    "\n",
    "funnel = Funnel(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "funnel.add(\n",
    "        series_name=\"\",\n",
    "        data_pair=my_data\n",
    "    )\n",
    "funnel.set_global_opts(title_opts=opts.TitleOpts(title=\"整体各环节漏斗图\",subtitle='浏览-收藏-加购-购买各环节人数'))\n",
    "funnel.set_series_opts(\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show=True,\n",
    "        formatter=\"{a}\\n{b} : {c}\",\n",
    "        position = \"inside\",\n",
    "        font_weight = 'bolder',\n",
    "        font_style = 'oblique',\n",
    "        font_size=15,\n",
    "        ), # 是否显示数据标签                        \n",
    ")\n",
    "funnel.load_javascript()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcb9008f-4089-4966-b69d-0c02a1fb11c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"bc9f91b6470d49c792838d62186df74a\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_bc9f91b6470d49c792838d62186df74a = echarts.init(\n",
       "            document.getElementById('bc9f91b6470d49c792838d62186df74a'), 'dark', {renderer: 'canvas'});\n",
       "        var option_bc9f91b6470d49c792838d62186df74a = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"funnel\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u6d4f\\u89c8\",\n",
       "                    \"value\": 11550581\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6536\\u85cf\",\n",
       "                    \"value\": 343564\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u52a0\\u8d2d\\u7269\\u8f66\",\n",
       "                    \"value\": 242556\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d2d\\u4e70\",\n",
       "                    \"value\": 120205\n",
       "                }\n",
       "            ],\n",
       "            \"sort\": \"descending\",\n",
       "            \"gap\": 0,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 15,\n",
       "                \"fontStyle\": \"oblique\",\n",
       "                \"fontWeight\": \"bolder\",\n",
       "                \"formatter\": \"{a}\\n{b} : {c}\"\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"shadowBlur\": 8,\n",
       "                    \"barBorderRadius\": [\n",
       "                        100,\n",
       "                        100,\n",
       "                        100,\n",
       "                        100\n",
       "                    ],\n",
       "                    \"shadowColor\": \"#E9B7D3\",\n",
       "                    \"shadowOffsetY\": 6,\n",
       "                    \"shadowOffsetX\": 6\n",
       "                }\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"funnel\",\n",
       "            \"name\": \" \",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u6d4f\\u89c8\",\n",
       "                    \"value\": 11550581\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6536\\u85cf\",\n",
       "                    \"value\": 343564\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u52a0\\u8d2d\\u7269\\u8f66\",\n",
       "                    \"value\": 242556\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d2d\\u4e70\",\n",
       "                    \"value\": 120205\n",
       "                }\n",
       "            ],\n",
       "            \"sort\": \"descending\",\n",
       "            \"gap\": 0,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 15,\n",
       "                \"fontStyle\": \"oblique\",\n",
       "                \"fontWeight\": \"bolder\",\n",
       "                \"formatter\": \"{a}\\n{b} : {c}\"\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"shadowBlur\": 8,\n",
       "                    \"barBorderRadius\": [\n",
       "                        100,\n",
       "                        100,\n",
       "                        100,\n",
       "                        100\n",
       "                    ],\n",
       "                    \"shadowColor\": \"#E9B7D3\",\n",
       "                    \"shadowOffsetY\": 6,\n",
       "                    \"shadowOffsetX\": 6\n",
       "                }\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u52a0\\u8d2d\\u7269\\u8f66\",\n",
       "                \"\\u6d4f\\u89c8\",\n",
       "                \"\\u6536\\u85cf\",\n",
       "                \"\\u8d2d\\u4e70\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u6d4f\\u89c8\": true,\n",
       "                \"\\u6536\\u85cf\": true,\n",
       "                \"\\u52a0\\u8d2d\\u7269\\u8f66\": true,\n",
       "                \"\\u8d2d\\u4e70\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"left\": \"right\",\n",
       "            \"top\": \"3%\",\n",
       "            \"orient\": \"vertical\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"textStyle\": {\n",
       "                \"color\": \"#5A3147\",\n",
       "                \"fontWeight\": \"bolder\",\n",
       "                \"fontSize\": \"13\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u6574\\u4f53\\u5404\\u73af\\u8282\\u6f0f\\u6597\\u56fe\",\n",
       "            \"subtext\": \"\\u6d4f\\u89c8-\\u6536\\u85cf-\\u52a0\\u8d2d-\\u8d2d\\u4e70\\u5404\\u73af\\u8282\\u4eba\\u6570\",\n",
       "            \"left\": \"center\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"textStyle\": {\n",
       "                \"color\": \"#5A3147\"\n",
       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"color\": \"#5A3147\"\n",
       "            }\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_bc9f91b6470d49c792838d62186df74a.setOption(option_bc9f91b6470d49c792838d62186df74a);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22d1741d9a0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate = ['浏览','收藏','加购物车','购买']\n",
    "y_data = [int(i) for i in list(data['behavior_type'].value_counts().values)]\n",
    "\n",
    "funnel.add(\n",
    "    \" \", \n",
    "    [list(z) for z in zip(cate, y_data)],\n",
    "    # is_label_show=True,  # 确认显示标签\n",
    "    )\n",
    "funnel.set_series_opts( # 自定义图表样式\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show=True,\n",
    "        formatter=\"{a}\\n{b} : {c}\",\n",
    "        position = \"inside\",\n",
    "        font_weight = 'bolder',\n",
    "        font_style = 'oblique',\n",
    "        font_size=15,\n",
    "        ), # 是否显示数据标签\n",
    "    itemstyle_opts={  \n",
    "        \"normal\": {\n",
    "     # 调整柱子颜色渐变\n",
    "            'shadowBlur': 8,   # 光影大小\n",
    "            \"barBorderRadius\": [100, 100, 100, 100],  # 调整柱子圆角弧度\n",
    "            \"shadowColor\": \"#E9B7D3\", # 调整阴影颜色\n",
    "            'shadowOffsetY': 6,\n",
    "            'shadowOffsetX': 6,  # 偏移量\n",
    "        }\n",
    "    }\n",
    ")\n",
    "funnel.set_global_opts(\n",
    "# 标题设置\n",
    "    title_opts=opts.TitleOpts(\n",
    "        title='整体各环节漏斗图', # 主标题\n",
    "        subtitle='浏览-收藏-加购-购买各环节人数', # 副标题\n",
    "        pos_left='center',  # 标题展示位置\n",
    "        title_textstyle_opts=dict(color='#5A3147'), # 设置标题字体颜色\n",
    "        subtitle_textstyle_opts=dict(color='#5A3147')\n",
    "    ),\n",
    "    legend_opts=opts.LegendOpts(\n",
    "        is_show=True, # 是否显示图例\n",
    "        pos_left='right', # 图例显示位置\n",
    "        pos_top='3%',  #图例距离顶部的距离\n",
    "        orient='vertical',  # 图例水平布局\n",
    "        textstyle_opts=opts.TextStyleOpts(\n",
    "            color='#5A3147',  # 颜色\n",
    "            font_size='13',   # 字体大小\n",
    "            font_weight='bolder',   # 加粗\n",
    "    ),\n",
    "    ),\n",
    ")\n",
    "funnel.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf1da50e-2ee5-4ab8-a305-6c1924438660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"e600043f955e4031851ee64b1dfce84c\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_e600043f955e4031851ee64b1dfce84c = echarts.init(\n",
       "            document.getElementById('e600043f955e4031851ee64b1dfce84c'), 'dark', {renderer: 'canvas'});\n",
       "        var option_e600043f955e4031851ee64b1dfce84c = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"funnel\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u6d4f\\u89c8\",\n",
       "                    \"value\": 11550581\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6536\\u85cf\",\n",
       "                    \"value\": 343564\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u52a0\\u8d2d\\u7269\\u8f66\",\n",
       "                    \"value\": 242556\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d2d\\u4e70\",\n",
       "                    \"value\": 120205\n",
       "                }\n",
       "            ],\n",
       "            \"sort\": \"descending\",\n",
       "            \"gap\": 0,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u6536\\u85cf\",\n",
       "                \"\\u52a0\\u8d2d\\u7269\\u8f66\",\n",
       "                \"\\u6d4f\\u89c8\",\n",
       "                \"\\u8d2d\\u4e70\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u6d4f\\u89c8\": true,\n",
       "                \"\\u6536\\u85cf\": true,\n",
       "                \"\\u52a0\\u8d2d\\u7269\\u8f66\": true,\n",
       "                \"\\u8d2d\\u4e70\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u7528\\u6237\\u884c\\u4e3a\\u6f0f\\u6597\\u56fe\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_e600043f955e4031851ee64b1dfce84c.setOption(option_e600043f955e4031851ee64b1dfce84c);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22c52082a60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funnel.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab31ac1-4766-4c75-94e2-3c31f86c8484",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 流量分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcdea0e5-1356-4957-bf05-1943334def1a",
   "metadata": {},
   "source": [
    "从宏观的流量分析入手，来看看能发现哪有有趣的规律。我们主要关注访问量(PV)与独立访问量(UV)：\n",
    "- 访问量(PV)：全名为Page View, 基于用户每次对淘宝页面的刷新次数，用户每刷新一次页面或者打开新的页面就记录就算一次访问。\n",
    "- 独立访问量(UV)：全名为Unique Visitor，一个用户若多次访问淘宝只记录一次，熟悉SQL的小伙伴会知道，本质上是unique操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1903b80e-cea6-456d-85d1-cdca97ca8c39",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 基于天级别访问流量分析  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ec7f37a-2079-479b-8e91-9e36b5393cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>pv_daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>366701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>358823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-20</td>\n",
       "      <td>353429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>333104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-22</td>\n",
       "      <td>361355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-11-23</td>\n",
       "      <td>382702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-11-24</td>\n",
       "      <td>378342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-11-25</td>\n",
       "      <td>370239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>360896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-11-27</td>\n",
       "      <td>371384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  pv_daily\n",
       "0 2014-11-18    366701\n",
       "1 2014-11-19    358823\n",
       "2 2014-11-20    353429\n",
       "3 2014-11-21    333104\n",
       "4 2014-11-22    361355\n",
       "5 2014-11-23    382702\n",
       "6 2014-11-24    378342\n",
       "7 2014-11-25    370239\n",
       "8 2014-11-26    360896\n",
       "9 2014-11-27    371384"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算PV: PageView , UV: UniqueView\n",
    "# PV: select count(user_id) from data_user group by \"date\";\n",
    "# UV: select count(distinct(user_id)) from data_user group by \"date\";\n",
    "\n",
    "pv_daily = data.groupby('date')['user_id'].count()\n",
    "\n",
    "pv_daily = pv_daily.reset_index() \n",
    "pv_daily = pv_daily.rename(columns={'user_id':'pv_daily'})\n",
    "pv_daily.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6465e932-3353-4b8c-b6b7-8657817032e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>uv_daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>6343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>6420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-20</td>\n",
       "      <td>6333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>6276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-22</td>\n",
       "      <td>6187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-11-23</td>\n",
       "      <td>6373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-11-24</td>\n",
       "      <td>6513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-11-25</td>\n",
       "      <td>6351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>6357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-11-27</td>\n",
       "      <td>6359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  uv_daily\n",
       "0 2014-11-18      6343\n",
       "1 2014-11-19      6420\n",
       "2 2014-11-20      6333\n",
       "3 2014-11-21      6276\n",
       "4 2014-11-22      6187\n",
       "5 2014-11-23      6373\n",
       "6 2014-11-24      6513\n",
       "7 2014-11-25      6351\n",
       "8 2014-11-26      6357\n",
       "9 2014-11-27      6359"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算UV\n",
    "\n",
    "uv_daily = data.groupby('date')['user_id'].apply(lambda x: len(x.unique()))\n",
    "uv_daily = uv_daily.reset_index()\n",
    "uv_daily = uv_daily.rename(columns = {'user_id':'uv_daily'})\n",
    "uv_daily.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3670a4e3-2f3c-4613-a4b8-e10a1cbee4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts.charts import Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6d9ba08-f289-4e6d-b32b-edb072f365d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"80d9c1205957402ba261b6049e713672\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_80d9c1205957402ba261b6049e713672 = echarts.init(\n",
       "            document.getElementById('80d9c1205957402ba261b6049e713672'), 'dark', {renderer: 'canvas'});\n",
       "        var option_80d9c1205957402ba261b6049e713672 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"connectNulls\": false,\n",
       "            \"symbolSize\": 4,\n",
       "            \"showSymbol\": true,\n",
       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
       "            \"step\": false,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"11-18\",\n",
       "                    366701\n",
       "                ],\n",
       "                [\n",
       "                    \"11-19\",\n",
       "                    358823\n",
       "                ],\n",
       "                [\n",
       "                    \"11-20\",\n",
       "                    353429\n",
       "                ],\n",
       "                [\n",
       "                    \"11-21\",\n",
       "                    333104\n",
       "                ],\n",
       "                [\n",
       "                    \"11-22\",\n",
       "                    361355\n",
       "                ],\n",
       "                [\n",
       "                    \"11-23\",\n",
       "                    382702\n",
       "                ],\n",
       "                [\n",
       "                    \"11-24\",\n",
       "                    378342\n",
       "                ],\n",
       "                [\n",
       "                    \"11-25\",\n",
       "                    370239\n",
       "                ],\n",
       "                [\n",
       "                    \"11-26\",\n",
       "                    360896\n",
       "                ],\n",
       "                [\n",
       "                    \"11-27\",\n",
       "                    371384\n",
       "                ],\n",
       "                [\n",
       "                    \"11-28\",\n",
       "                    340638\n",
       "                ],\n",
       "                [\n",
       "                    \"11-29\",\n",
       "                    364697\n",
       "                ],\n",
       "                [\n",
       "                    \"11-30\",\n",
       "                    401620\n",
       "                ],\n",
       "                [\n",
       "                    \"12-01\",\n",
       "                    394611\n",
       "                ],\n",
       "                [\n",
       "                    \"12-02\",\n",
       "                    405216\n",
       "                ],\n",
       "                [\n",
       "                    \"12-03\",\n",
       "                    411606\n",
       "                ],\n",
       "                [\n",
       "                    \"12-04\",\n",
       "                    399952\n",
       "                ],\n",
       "                [\n",
       "                    \"12-05\",\n",
       "                    361878\n",
       "                ],\n",
       "                [\n",
       "                    \"12-06\",\n",
       "                    389610\n",
       "                ],\n",
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     },
     "execution_count": 18,
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   ],
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    "x_data = [str(i)[5:10] for i in pv_daily['date'].tolist()]\n",
    "y_data_pv = pv_daily['pv_daily'].tolist()\n",
    "y_data_uv = uv_daily['uv_daily'].tolist()\n",
    "\n",
    "line = Line(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "line.add_xaxis(x_data)\n",
    "line.add_yaxis('', y_data_pv, label_opts=opts.LabelOpts(is_show=False))\n",
    "line.set_global_opts(title_opts=opts.TitleOpts(title=\"基于天级别访问流量分析\", subtitle='Pv_daily'))\n",
    "\n",
    "line.render_notebook()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "face6878-d36a-4746-954b-f157212e60ab",
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   "source": [
    "x_data = [str(i)[5:10] for i in uv_daily['date'].tolist()]\n",
    "y_data_uv = uv_daily['uv_daily'].tolist()\n",
    "\n",
    "line = Line(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "line.add_xaxis(x_data)\n",
    "line.add_yaxis('', y_data_uv, label_opts=opts.LabelOpts(is_show=False))\n",
    "line.set_global_opts(title_opts=opts.TitleOpts(title=\"基于天级别访问流量分析\", subtitle='Uv_daily'))\n",
    "\n",
    "line.render_notebook()"
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  {
   "cell_type": "markdown",
   "id": "5056aa2d-dcf9-4e45-adf1-efa7b5d37051",
   "metadata": {},
   "source": [
    "可以看出，不管是PV还是UV趋势，均在12号的时候出现了一个尖峰，这正是著名的双十二大促节的用户集中消费导致的变化。  \n",
    "通过简单的数据分析和可视化工具，这是我们从数据中观察到了第一个结论。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9973bc0-ff0d-4c48-9377-ca307dbd9b2a",
   "metadata": {},
   "source": [
    "### 基于小时级别访问流量分析  \n",
    "\n",
    "上面的对不同访问量进行分析，其分析的时间跨度是每天。另外从我们的直觉可以知道，用户在一天当中对淘宝的使用也是有一定规律的。为了探索这个规律，我们将按照每小时统计用户的访问量和独立访问量。同pv_daily, uv_daily分析，我们完成如下代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f7e5def3-2ac2-46f2-aeeb-6217a7b0c116",
   "metadata": {},
   "outputs": [
    {
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       "   hour  pv_hour\n",
       "0     0   517404\n",
       "1     1   267682\n",
       "2     2   147090\n",
       "3     3    98516\n",
       "4     4    80487"
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     "metadata": {},
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   ],
   "source": [
    "# 计算每小时的PV\n",
    "\n",
    "pv_hour = data.groupby('hour')['user_id'].count()\n",
    "pv_hour = pv_hour.reset_index().rename(columns={'user_id':'pv_hour'})\n",
    "pv_hour.head()"
   ]
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   "execution_count": 21,
   "id": "c15edbf5-158f-4c6a-bcb1-8da71dc2dcf1",
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       "      <th>hour</th>\n",
       "      <th>uv_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  uv_hour\n",
       "0     0     5786\n",
       "1     1     3780\n",
       "2     2     2532\n",
       "3     3     1937\n",
       "4     4     1765"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算每小时的UV\n",
    "\n",
    "uv_hour = data.groupby('hour')['user_id'].apply(lambda x:len(x.unique()))\n",
    "uv_hour = uv_hour.reset_index().rename(columns={'user_id':'uv_hour'})\n",
    "uv_hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b513d5f-7a8a-4158-9dbc-25e51a3d44a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hour_pv_uv(df, col):\n",
    "    x_data = [str(i) for i in df['hour'].tolist()]\n",
    "    y_data = df[col].tolist()\n",
    "    \n",
    "    line = Line(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "    line.add_xaxis(x_data)\n",
    "    line.add_yaxis('', y_data, \n",
    "                       label_opts=opts.LabelOpts(is_show=False),\n",
    "                      markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_=\"average\")]))\n",
    "    line.set_global_opts(title_opts=opts.TitleOpts(title=\"基于小时级别访问流量分析\", subtitle=col))\n",
    "    \n",
    "    return line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "674774ed-8f18-4044-8ad0-7ef98b312f74",
   "metadata": {},
   "outputs": [
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       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
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       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
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       "</body>\n",
       "</html>\n"
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       "<pyecharts.render.display.HTML at 0x22cebb56a30>"
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     },
     "execution_count": 23,
     "metadata": {},
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   ],
   "source": [
    "line = plot_hour_pv_uv(pv_hour, col='pv_hour')\n",
    "line.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0dbab27c-5acf-4f9b-b51d-f6a1ad84b9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
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       "            \"nameGap\": 15,\n",
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       "            \"minInterval\": 0,\n",
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       "                    \"type\": \"solid\"\n",
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       "            \"text\": \"\\u57fa\\u4e8e\\u5c0f\\u65f6\\u7ea7\\u522b\\u8bbf\\u95ee\\u6d41\\u91cf\\u5206\\u6790\",\n",
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       "};\n",
       "        chart_7fff88939d704e0dadd7e8999e578679.setOption(option_7fff88939d704e0dadd7e8999e578679);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22cebacce50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line = plot_hour_pv_uv(uv_hour, col='uv_hour')\n",
    "line.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecd028e-5228-46f8-bbfd-0e7341f647dc",
   "metadata": {},
   "source": [
    "可以看出，PV的高峰值出现在20点之后，可能的原因是淘宝的主力消费人群是工薪阶层，这部分群体在下班后开始使用淘宝浏览购物；UV的值比较恒定，上午10点之后便没有出现大的波动，一个可能的原因是用户早晨也会刷一下淘宝，比如看看物流状态，UV值在一天之内就不会再有大的变化波动了。  \n",
    "另外也可以看出，凌晨2点之后，PV/UV的趋势一致，均是一天中流量最小的时间段。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f56076e-344c-4200-b443-c72e616a96c5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 双十二当日基于小时的PV、UV分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b91d2a9-cbb5-426d-ad5f-acc86f31a35a",
   "metadata": {},
   "source": [
    "从“日PV/UV”趋势看，双十二当天的总体流量会出现明显的峰值。那么双十二当天基于小时的用户访问数据会有变化吗？我们来写代码分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c718006e-a8ba-4a44-b590-3d9ad5de5425",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user_1212 = data.loc[data['date']=='2014-12-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3768f617-d538-43cc-8fb8-90c1e512e0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>1212_pv_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>22761</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>11754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5168</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   hour  1212_pv_hour\n",
       "0     0         50030\n",
       "1     1         22761\n",
       "2     2         11754\n",
       "3     3          6173\n",
       "4     4          5168"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算每小时的PV、UV\n",
    "\n",
    "pv_hour_1212 = data_user_1212.groupby('hour')['user_id'].count().reset_index().rename(columns={'user_id':'1212_pv_hour'})\n",
    "uv_hour_1212 = data_user_1212.groupby('hour')['user_id'].apply(lambda x: len(x.unique())).reset_index().rename(columns={'user_id':'1212_uv_hour'})\n",
    "pv_hour_1212.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d3e4ba-b363-4313-aebf-4b16c144f291",
   "metadata": {},
   "source": [
    "为了方便对比，我们和30日总体的小时级别PV/UV变化趋势做对比："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1cec9b44-2836-468c-9d73-d9f08906ff92",
   "metadata": {},
   "outputs": [
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       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
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       "        },\n",
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       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
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       "                \"lineStyle\": {\n",
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       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u57fa\\u4e8e\\u5c0f\\u65f6\\u7ea7\\u522b\\u8bbf\\u95ee\\u6d41\\u91cf\\u5206\\u6790\",\n",
       "            \"subtext\": \"1212_pv_hour\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_758f856c0069491486d256d0440c8013.setOption(option_758f856c0069491486d256d0440c8013);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22cebab7910>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Page\n",
    "\n",
    "\n",
    "page=Page()\n",
    "page.add(plot_hour_pv_uv(pv_hour, col='pv_hour'), plot_hour_pv_uv(pv_hour_1212, col='1212_pv_hour'))\n",
    "page.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f88bc744-2cce-4655-aa6e-6e74a4acd364",
   "metadata": {},
   "outputs": [
    {
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       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"fbe7e905b2f84fd1b8d487c282e304a6\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
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      ],
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       "<pyecharts.render.display.HTML at 0x22ceba74c40>"
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     },
     "execution_count": 28,
     "metadata": {},
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    }
   ],
   "source": [
    "page=Page()\n",
    "page.add(plot_hour_pv_uv(uv_hour, col='uv_hour'), plot_hour_pv_uv(uv_hour_1212, col='1212_uv_hour'))\n",
    "page.render_notebook()"
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  },
  {
   "cell_type": "markdown",
   "id": "a06a5b29-3be2-4f95-8eda-4439e0d0cbfa",
   "metadata": {},
   "source": [
    "可以看到，双十二当天，PV变化趋与一个月内的PV变化趋势基本一致，只不过曲线不太平滑，多出来小的凸点，一个可能的原因是大促当天，有整点领券活动，通过推送等形式会有效拉动用户使用淘宝。UV变化趋势稍有不同，可以看到双十二当天在晚上8点之后UV出现了小高峰，表明了大促当天用户的消费意愿还是比较强烈。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db3be2b-6608-4e47-8aa1-c89a1a85bfe9",
   "metadata": {},
   "source": [
    "### 不同用户行为流量分析  \n",
    "\n",
    "众所周知，淘宝用户分布广泛，不同的用户的购物行为有较大差异。比如不同的用户的登录习惯以及购买偏好，因此按照用户的角度进行分析是重要且有趣的。因此我们依旧从用户访问量的这一数据来分析不同用户的浏览淘宝的习惯。  \n",
    "\n",
    "为了获得用户的行为模式，我们主要统计在一天当中（按照每小时）用户发生的行为。因此我们使用`groupby`进行数据分组，方式如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "340233d6-4a3f-478b-81e9-241b58d1473d",
   "metadata": {},
   "outputs": [
    {
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       "   behavior_type  hour  pv_behavior\n",
       "0              1     0       487341\n",
       "1              1     1       252991\n",
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       "3              1     3        93250\n",
       "4              1     4        75832\n",
       "5              1     5        83545\n",
       "6              1     6       150356\n",
       "7              1     7       272470\n",
       "8              1     8       374701\n",
       "9              1     9       456781"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 基于 behavior_type & hour 分组\n",
    "# 点击、收藏、加购物车、支付四种行为，分别用数字1、2、3、4表示\n",
    "pv_behavior = data.groupby(['behavior_type','hour'])['user_id'].count()\n",
    "pv_behavior = pv_behavior.reset_index()\n",
    "pv_behavior = pv_behavior.rename(columns={'user_id':'pv_behavior'})\n",
    "pv_behavior.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3468fc72-4e9a-497b-b6b9-26d36299d14e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "27f97eab-0ddd-41d8-a3fe-794206c03d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "# sns: serborn\n",
    "ax = sns.lineplot(x='hour',y='pv_behavior',hue='behavior_type',data=pv_behavior)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddaaddb-cdf4-4594-84d1-fadab9c930a8",
   "metadata": {},
   "source": [
    "因为action_type为1（浏览行为）的占比非常大，导致上图其它几类action的趋势不太明显，我们去掉action_type为1的数据后再来看看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8ede1382-1256-4422-b419-30151e04c845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hour', ylabel='pv_behavior'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='hour',y='pv_behavior',hue='behavior_type',data=pv_behavior[pv_behavior.behavior_type!=1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07442823-77dc-4d0a-95f7-45311ed4a0f5",
   "metadata": {},
   "source": [
    "可以看出4种行为按照小时的变化趋势基本一致，都是在晚上8点之后有明显增长，凌晨2-6点是低峰，符合常识。感兴趣的小伙伴可以进一步基于双十二当天的数据来分析下4种行为的变化趋，看看有什么新的发现？  \n",
    "\n",
    "我们在充分了解用户分时的数据趋势后，可以针对性的进行营销活动，如推送优惠劵等，大家也可以留意下日常使用电商类App的推送通知的推送时间，一般都是在用户高峰期来做推送，可进一步促进转化率。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0342b5-ebf3-451d-a18a-7672a8e52b3b",
   "metadata": {},
   "source": [
    "#### 基于双十二当天的数据来分析下4种行为的变化趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "27efa31d-24d4-4c55-a291-f9e70b49ac3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>101260672</td>\n",
       "      <td>212072908</td>\n",
       "      <td>1</td>\n",
       "      <td>10984</td>\n",
       "      <td>2014-12-12 11</td>\n",
       "      <td>2014-12-12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>101781721</td>\n",
       "      <td>19349307</td>\n",
       "      <td>1</td>\n",
       "      <td>1863</td>\n",
       "      <td>2014-12-12 12</td>\n",
       "      <td>2014-12-12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>100684618</td>\n",
       "      <td>94486594</td>\n",
       "      <td>1</td>\n",
       "      <td>10984</td>\n",
       "      <td>2014-12-12 23</td>\n",
       "      <td>2014-12-12</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>103802946</td>\n",
       "      <td>190848347</td>\n",
       "      <td>1</td>\n",
       "      <td>5232</td>\n",
       "      <td>2014-12-12 22</td>\n",
       "      <td>2014-12-12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>104811265</td>\n",
       "      <td>354843735</td>\n",
       "      <td>1</td>\n",
       "      <td>10585</td>\n",
       "      <td>2014-12-12 21</td>\n",
       "      <td>2014-12-12</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id    item_id  behavior_type item_category           time  \\\n",
       "13  101260672  212072908              1         10984  2014-12-12 11   \n",
       "20  101781721   19349307              1          1863  2014-12-12 12   \n",
       "54  100684618   94486594              1         10984  2014-12-12 23   \n",
       "69  103802946  190848347              1          5232  2014-12-12 22   \n",
       "95  104811265  354843735              1         10585  2014-12-12 21   \n",
       "\n",
       "         date  hour  \n",
       "13 2014-12-12    11  \n",
       "20 2014-12-12    12  \n",
       "54 2014-12-12    23  \n",
       "69 2014-12-12    22  \n",
       "95 2014-12-12    21  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_1212.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3fcea5e5-79ac-405c-b17a-654340e40adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>hour</th>\n",
       "      <th>pv_behavior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>20502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>25891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   behavior_type  hour  pv_behavior\n",
       "0              1     0        45229\n",
       "1              1     1        20988\n",
       "2              1     2        10896\n",
       "3              1     3         5762\n",
       "4              1     4         4827\n",
       "5              1     5         6050\n",
       "6              1     6        11997\n",
       "7              1     7        20502\n",
       "8              1     8        25891\n",
       "9              1     9        30066"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_behavior_1212 = data_user_1212.groupby(['behavior_type','hour'])['user_id'].count()\n",
    "pv_behavior_1212 = pv_behavior_1212.reset_index()\n",
    "pv_behavior_1212 = pv_behavior_1212.rename(columns={'user_id':'pv_behavior'})\n",
    "pv_behavior_1212.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fc25fb3c-7ed1-44b5-86c2-e237cd2562ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pv_behavior_1212')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 'SimHei'是中文字体文件的名称\n",
    "\n",
    "fig, axes = plt.subplots(2,1,sharex=True)\n",
    "pv_behavior[pv_behavior.behavior_type==1].plot(x='hour',y='pv_behavior',ax=axes[0],colormap='cividis')\n",
    "pv_behavior_1212[pv_behavior_1212.behavior_type==1].plot(x='hour', y='pv_behavior', ax=axes[1],colormap='RdGy')\n",
    "\n",
    "fig.suptitle('用户发生浏览行为的PV对比图')\n",
    "\n",
    "axes[0].set_title('pv_behavior')\n",
    "axes[1].set_title('pv_behavior_1212')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ec330b42-9a5a-4ae7-8d97-9036ec35d079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pv_behavior_1212')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 'SimHei'是中文字体文件的名称\n",
    "\n",
    "fig, axes = plt.subplots(2,1,sharex=True)\n",
    "\n",
    "sns.lineplot(x='hour',y='pv_behavior',hue='behavior_type',data=pv_behavior[pv_behavior.behavior_type!=1],ax=axes[0])\n",
    "sns.lineplot(x='hour',y='pv_behavior',hue='behavior_type',data=pv_behavior_1212[pv_behavior_1212.behavior_type!=1],ax=axes[1])\n",
    "\n",
    "fig.suptitle('用户发生其他行为的PV对比图')\n",
    "\n",
    "axes[0].set_title('pv_behavior')\n",
    "axes[1].set_title('pv_behavior_1212')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3345169b-acda-425d-bc27-2e454bb5eceb",
   "metadata": {},
   "source": [
    "- 可以看出在双十二当天浏览用户和当月浏览用户变化趋势基本吻合\n",
    "- 但在支付用户中，双十二当天的支付用户要超过收藏商品的用户。在活动日外则是收藏商品用户要高于支付用户"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81645a04-b86e-4b86-a0a2-8fe3a1014810",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 转化率分析  \n",
    "\n",
    "我们来分析“浏览-收藏/加购-购买”链路的转化漏斗模型，可以帮助我们更好的了解各个环节的转化链路：  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c72c82d9-7e0e-4522-a6c2-bfcc5900eb01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "behavior_type\n",
       "1    11550581\n",
       "2      242556\n",
       "3      343564\n",
       "4      120205\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_type = data.groupby(['behavior_type'])['user_id'].count()\n",
    "\n",
    "behavior_type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18972e0-cad2-4731-a546-eaee2eb3bc10",
   "metadata": {},
   "source": [
    "可以看到用户浏览（action_type=1）的基数是最大的，而加购和收藏没有必然联系，因此我们把这两类合并在一起做分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1b094b17-2559-4d7b-b4ab-ff253db97ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "click_num = behavior_type[1]\n",
    "fav_num = behavior_type[2]\n",
    "add_num = behavior_type[3]\n",
    "pay_num = behavior_type[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "93ab9327-100d-44d4-a897-d619a83a040a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 收藏+加购物车用户数\n",
    "fav_add_num = fav_num + add_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "93f16e28-6208-43d2-a583-aff4b7ea27d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "view_cart_rate = round(100 * fav_add_num / click_num,2)\n",
    "cart_pay_rate = round(100 * pay_num / fav_add_num,2)\n",
    "view_pay_rate = round(100 * pay_num / click_num,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "17feb725-33a2-4a91-8dbe-4fa26c9e030d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyecharts.charts import Gauge\n",
    "\n",
    "# 整体转换率的查看\n",
    "def chart_gauge(num,title = '主标题',label = '图例'):\n",
    "    gauge = Gauge(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "    gauge.add(\n",
    "            label, \n",
    "            [(title,num)],\n",
    "            min_ = 0,   # 最小的数据值\n",
    "            max_ = 40,  # 最大的数据值\n",
    "            radius =  \"75%\",  # 仪表盘半径\n",
    "            title_label_opts=opts.LabelOpts(\n",
    "                font_size=20,\n",
    "                color=\"#ECBBB5\", \n",
    "                font_family=\"Microsoft YaHei\",  # 设置字体、颜色、大小\n",
    "                font_weight = \"bolder\",\n",
    "            ),\n",
    "            detail_label_opts = opts.GaugeDetailOpts(  # 配置数字显示位置以及字体、颜色等\n",
    "                is_show=True,\n",
    "                offset_center = [0,'40%'],  # 数字相对位置，可以是绝对数值，也可以是百分比 \n",
    "                formatter=\"{}%\".format(num),       # 文字格式化\n",
    "                font_style = \"oblique\",  # 文字风格\n",
    "                font_weight = \"bold\",  #字重\n",
    "                font_size = 35,\n",
    "                ),\n",
    "            )\n",
    "    gauge.set_global_opts(\n",
    "        legend_opts=opts.LegendOpts(\n",
    "            is_show=False\n",
    "        ), \n",
    "    )\n",
    "    return gauge.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "61ec66a6-a290-47cc-a694-a3d8609cde34",
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"2390efd7cec54c808ac0875bb4559b75\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_2390efd7cec54c808ac0875bb4559b75 = echarts.init(\n",
       "            document.getElementById('2390efd7cec54c808ac0875bb4559b75'), 'dark', {renderer: 'canvas'});\n",
       "        var option_2390efd7cec54c808ac0875bb4559b75 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"gauge\",\n",
       "            \"title\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"color\": \"#ECBBB5\",\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 20,\n",
       "                \"fontWeight\": \"bolder\",\n",
       "                \"fontFamily\": \"Microsoft YaHei\"\n",
       "            },\n",
       "            \"detail\": {\n",
       "                \"show\": true,\n",
       "                \"backgroundColor\": \"transparent\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderColor\": \"transparent\",\n",
       "                \"offsetCenter\": [\n",
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       "                    \"40%\"\n",
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       "                \"formatter\": \"5.07%\",\n",
       "                \"color\": \"auto\",\n",
       "                \"fontStyle\": \"oblique\",\n",
       "                \"fontWeight\": \"bold\",\n",
       "                \"fontFamily\": \"sans-serif\",\n",
       "                \"fontSize\": 35,\n",
       "                \"borderRadius\": 0,\n",
       "                \"padding\": 0,\n",
       "                \"shadowColor\": \"transparent\",\n",
       "                \"shadowBlur\": 0,\n",
       "                \"shadowOffsetX\": 0,\n",
       "                \"shadowOffsetY\": 0\n",
       "            },\n",
       "            \"name\": \"\\u8f6c\\u5316\\u7387\",\n",
       "            \"min\": 0,\n",
       "            \"max\": 40,\n",
       "            \"splitNumber\": 10,\n",
       "            \"radius\": \"75%\",\n",
       "            \"startAngle\": 225,\n",
       "            \"endAngle\": -45,\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u6d4f\\u89c8-\\u52a0\\u8d2d/\\u6536\\u85cf\\u603b\\u4f53\\u8f6c\\u5316\\u7387\",\n",
       "                    \"value\": 5.07\n",
       "                }\n",
       "            ],\n",
       "            \"pointer\": {\n",
       "                \"show\": true,\n",
       "                \"length\": \"80%\",\n",
       "                \"width\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u8f6c\\u5316\\u7387\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u8f6c\\u5316\\u7387\": true\n",
       "            },\n",
       "            \"show\": false,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_2390efd7cec54c808ac0875bb4559b75.setOption(option_2390efd7cec54c808ac0875bb4559b75);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22d18bf54f0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart_gauge(view_cart_rate,title = '浏览-加购/收藏总体转化率',label = '转化率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "556c11cd-e0e1-432e-8767-5dcc59c1d0ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"8068c8c4232a4d03bf42eb59dbe77c59\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_8068c8c4232a4d03bf42eb59dbe77c59 = echarts.init(\n",
       "            document.getElementById('8068c8c4232a4d03bf42eb59dbe77c59'), 'dark', {renderer: 'canvas'});\n",
       "        var option_8068c8c4232a4d03bf42eb59dbe77c59 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"gauge\",\n",
       "            \"title\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"color\": \"#ECBBB5\",\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 20,\n",
       "                \"fontWeight\": \"bolder\",\n",
       "                \"fontFamily\": \"Microsoft YaHei\"\n",
       "            },\n",
       "            \"detail\": {\n",
       "                \"show\": true,\n",
       "                \"backgroundColor\": \"transparent\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderColor\": \"transparent\",\n",
       "                \"offsetCenter\": [\n",
       "                    0,\n",
       "                    \"40%\"\n",
       "                ],\n",
       "                \"formatter\": \"20.51%\",\n",
       "                \"color\": \"auto\",\n",
       "                \"fontStyle\": \"oblique\",\n",
       "                \"fontWeight\": \"bold\",\n",
       "                \"fontFamily\": \"sans-serif\",\n",
       "                \"fontSize\": 35,\n",
       "                \"borderRadius\": 0,\n",
       "                \"padding\": 0,\n",
       "                \"shadowColor\": \"transparent\",\n",
       "                \"shadowBlur\": 0,\n",
       "                \"shadowOffsetX\": 0,\n",
       "                \"shadowOffsetY\": 0\n",
       "            },\n",
       "            \"name\": \"\\u8f6c\\u5316\\u7387\",\n",
       "            \"min\": 0,\n",
       "            \"max\": 40,\n",
       "            \"splitNumber\": 10,\n",
       "            \"radius\": \"75%\",\n",
       "            \"startAngle\": 225,\n",
       "            \"endAngle\": -45,\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
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       "                    \"value\": 20.51\n",
       "                }\n",
       "            ],\n",
       "            \"pointer\": {\n",
       "                \"show\": true,\n",
       "                \"length\": \"80%\",\n",
       "                \"width\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u8f6c\\u5316\\u7387\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u8f6c\\u5316\\u7387\": true\n",
       "            },\n",
       "            \"show\": false,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_8068c8c4232a4d03bf42eb59dbe77c59.setOption(option_8068c8c4232a4d03bf42eb59dbe77c59);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22d188fe4f0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart_gauge(cart_pay_rate,title = '加购/收藏-购买转化率',label = '转化率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e07bf541-91b4-4656-a34c-f89bf3f91d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"b20de2a1f0de49baac4f27cc5e5b84ca\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_b20de2a1f0de49baac4f27cc5e5b84ca = echarts.init(\n",
       "            document.getElementById('b20de2a1f0de49baac4f27cc5e5b84ca'), 'dark', {renderer: 'canvas'});\n",
       "        var option_b20de2a1f0de49baac4f27cc5e5b84ca = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"gauge\",\n",
       "            \"title\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"color\": \"#ECBBB5\",\n",
       "                \"margin\": 8,\n",
       "                \"fontSize\": 20,\n",
       "                \"fontWeight\": \"bolder\",\n",
       "                \"fontFamily\": \"Microsoft YaHei\"\n",
       "            },\n",
       "            \"detail\": {\n",
       "                \"show\": true,\n",
       "                \"backgroundColor\": \"transparent\",\n",
       "                \"borderWidth\": 0,\n",
       "                \"borderColor\": \"transparent\",\n",
       "                \"offsetCenter\": [\n",
       "                    0,\n",
       "                    \"40%\"\n",
       "                ],\n",
       "                \"formatter\": \"1.04%\",\n",
       "                \"color\": \"auto\",\n",
       "                \"fontStyle\": \"oblique\",\n",
       "                \"fontWeight\": \"bold\",\n",
       "                \"fontFamily\": \"sans-serif\",\n",
       "                \"fontSize\": 35,\n",
       "                \"borderRadius\": 0,\n",
       "                \"padding\": 0,\n",
       "                \"shadowColor\": \"transparent\",\n",
       "                \"shadowBlur\": 0,\n",
       "                \"shadowOffsetX\": 0,\n",
       "                \"shadowOffsetY\": 0\n",
       "            },\n",
       "            \"name\": \"\\u8f6c\\u5316\\u7387\",\n",
       "            \"min\": 0,\n",
       "            \"max\": 40,\n",
       "            \"splitNumber\": 10,\n",
       "            \"radius\": \"75%\",\n",
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       "            \"endAngle\": -45,\n",
       "            \"clockwise\": true,\n",
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       "                    \"value\": 1.04\n",
       "                }\n",
       "            ],\n",
       "            \"pointer\": {\n",
       "                \"show\": true,\n",
       "                \"length\": \"80%\",\n",
       "                \"width\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u8f6c\\u5316\\u7387\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u8f6c\\u5316\\u7387\": true\n",
       "            },\n",
       "            \"show\": false,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "        chart_b20de2a1f0de49baac4f27cc5e5b84ca.setOption(option_b20de2a1f0de49baac4f27cc5e5b84ca);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22d192e1c70>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart_gauge(view_pay_rate,title = '总体转换率',label = '转化率')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09b863b-95cf-4b66-bc53-fb0d903cd75c",
   "metadata": {},
   "source": [
    "1.绘制各环节的销售漏斗图可以发现，用户从浏览-加入购物车的转换率较低，大部分用户在浏览了商品以后，就直接划走了，并不会加入购物车，浏览到加购的总体转换率只有5.07%，这个数据并不是很客观，原因有可能是商品推荐的并不是用户喜欢的想购买的，或者是用户在浏览了产品后，并不会被商品详情页吸引，因此需要进一步查看从浏览-加入购物车过程中，到底在哪一部分使得用户放弃了加入购物车。\n",
    "\n",
    "2.绘制各环节转化率的仪表盘，可以发现，加购到购买的转化率还可以，有20.51%，说明加入购物车的客户还是有比较强的购买欲，应该尽量做多活动，新品竞品吸引客户，引导客户加入购物车，提高购买量。\n",
    "\n",
    "3.总体购买率同样也不高，仅有1.04%，浏览-购买的转换率也不高，因此要优化对用户的推荐，尽量推荐其想要的产品，产品有降价、新品、活动等及时推送给用户，要注重维护平台老用户。加强会员管理等。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "144efb79-5276-47e4-aee2-5bc34e8c0e57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "点击 ——> 加购/收藏转化率： 5.074376778103197\n",
      "点击 ——> 购买转化率:  1.0406835811982098\n",
      "加购/收藏 ——> 购买转化率:  20.508598921722513\n"
     ]
    }
   ],
   "source": [
    "print('点击 ——> 加购/收藏转化率：', 100 * fav_add_num / click_num)\n",
    "print('点击 ——> 购买转化率: ', 100 * pay_num / click_num)\n",
    "print('加购/收藏 ——> 购买转化率: ', 100 * pay_num / fav_add_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "236e1645-6925-4551-9c17-522932263540",
   "metadata": {},
   "source": [
    "可以观察到，从浏览到加购/收藏的转化率大约5%；“加购/收藏”后到购买转化率大约为20%，这个比例还是还是非常高的，说明用户感兴趣的商品更容易成单。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426726cf-ba3a-483f-816d-0bfdf4193df0",
   "metadata": {},
   "source": [
    "再来看看双十二当天的数据，看看是否有不同的结论？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f6208038-6b00-43b1-be06-1a7c54bf5815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "双十二 点击 ——> 加购/收藏转化率： 5.448732437837779\n",
      "双十二 点击 ——> 购买转化率:  2.3773707847303305\n",
      "双十二 加购/收藏 ——> 购买转化率:  43.631630142472964\n"
     ]
    }
   ],
   "source": [
    "behavior_type = data_user_1212.groupby(['behavior_type'])['user_id'].count()\n",
    "\n",
    "click_num, fav_num, add_num, pay_num =  behavior_type[1], behavior_type[2], behavior_type[3], behavior_type[4]\n",
    "\n",
    "fav_add_num = fav_num + add_num \n",
    "print('双十二 点击 ——> 加购/收藏转化率：', 100 * fav_add_num / click_num)\n",
    "print('双十二 点击 ——> 购买转化率: ', 100 * pay_num / click_num)\n",
    "print('双十二 加购/收藏 ——> 购买转化率: ', 100 * pay_num / fav_add_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73898331-5b63-43f0-952d-70db63216d38",
   "metadata": {},
   "source": [
    "可以看出，双十二当天，加购/收藏 到 购买转化率是平时的2倍之多，此外，加购/收藏 的转化率也比平时高出不少，说明大促的运营活动对用户活跃度的转化起到了很好的促进作用。\n",
    "\n",
    "作为商家来讲，可以考虑在特定节日推出特定主题的优惠活动，是个有效的促活、转化的方式。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54d4b48-4f30-47a3-bf78-f83630bc0bd2",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 用户价值分析  \n",
    "\n",
    "在分析完整体变化趋势和转化率之后，商家更关注的是用户行为，什么用户是有价值的，什么用户是潜在用户？商业上已经有不少成熟的模型可供参考，如用户价值[RFM分析模型](https://baike.baidu.com/item/RFM%E6%A8%A1%E5%9E%8B/7070365)等，在此不做详细解释。本项目中我们还是从实际问题出发，站在用户**购买行为**的角度来探索用户价值：  \n",
    "\n",
    "\n",
    "### 用户购买频次分析  \n",
    "\n",
    "从上面的统计结果来看，用户的点击数据是最多的，但是从淘宝盈利的角度来说，只有购买行为是商家最为关注的，这里就会引入一个问题：在给定的数据集上，淘宝用户购买东西的频次有多少？基于这个问题，我们可以用统计的方法计算。计算结果如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "93e3b64a-8296-49fa-b349-f75313d11492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "4913      6\n",
       "6118      1\n",
       "7528      6\n",
       "7591     21\n",
       "12645     8\n",
       "Name: behavior_type, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy = data[data.behavior_type==4].groupby('user_id')['behavior_type'].count()\n",
    "data_user_buy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2c84fe9c-7697-46ed-8ff2-6239a3d62d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='user_id'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_user_buy.plot(x='user_id',y='buy_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc55f50-242a-4a8e-ae64-c932b6daff8a",
   "metadata": {},
   "source": [
    "X轴代表user_id，Y轴是每个用户的购买次数。可以看到，大部分用户的购买次数均不超过50次，这期间还包括了双十二当天的集中购物，排除双十二高峰，实际消费次数会更少。当然也有部分用户的购买次数超过百次，甚至有超过800次的，高频消费的用户可以看作是忠实的超级用户。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5f26dd-22d2-4199-89b1-eafc71e7b5fd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### ARPU分析  \n",
    "\n",
    "ARPU(Average Revenue Per User) 表示每日的收益在所有活跃用户中的转化。详细的描述为，每日的所有收益与每日的活跃的用户数量有关，因此可以通过ARPU来计算，每日活跃用户对每日收益的转化效率。  \n",
    "\n",
    "该数据集中没有对金额的体现。那我们可以对ARPU这个指标做一些修改，改为度量**平台活跃用户每日平均消费次数**。  \n",
    "\n",
    "计算公式为： ARPU = 每日消费总次数 / 每日活跃用户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "26c19aec-7ad2-4ba5-9a62-df7b6c348b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>4913</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>4913</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>7591</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>12645</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>54056</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  user_id  behavior_type  action\n",
       "0 2014-11-18     4913              1      27\n",
       "1 2014-11-18     4913              2       1\n",
       "2 2014-11-18     7591              1       4\n",
       "3 2014-11-18    12645              1      25\n",
       "4 2014-11-18    54056              1      13"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#给数据集中每一个用户赋值一个1，表示有登录操作\n",
    "data['action'] = 1 \n",
    "\n",
    "# 得到 date, user_id, behavior_type和计算用户每日的登录次数\n",
    "data_user_arpu = data.groupby(['date','user_id','behavior_type'])['action'].count()\n",
    "data_user_arpu = data_user_arpu.reset_index()\n",
    "data_user_arpu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4dffa209-06e1-4fe2-b756-f0d856766c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    0.588050\n",
       "2014-11-19    0.574143\n",
       "2014-11-20    0.546660\n",
       "2014-11-21    0.481358\n",
       "2014-11-22    0.577016\n",
       "dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算arpu，近似公式： ARPU = 每日消费次数 / 每日活跃用户数\n",
    "arpu = data_user_arpu.groupby('date').apply(lambda x:x[x['behavior_type']==4]['action'].sum() / len(x['user_id'].unique()) ) \n",
    "arpu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "790b7d75-01a0-4e56-b81a-ddea019a9eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ARPU')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arpu.plot(colormap='cividis')\n",
    "plt.title('ARPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28325218-b85d-40c2-accf-4a2b8da5e2fb",
   "metadata": {},
   "source": [
    "可以看到，活跃用户每天平均消费次数在0.5次左右，双十二期间达到最高值接近2，是平时的4倍左右，表明用户会集中在大促日的时候购买。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41ecd2a-e540-4440-8bd3-aeec4482897b",
   "metadata": {},
   "source": [
    "计算ARPU过程中，分子使用的是购买累计次数，如果分子只统计购买的用户数，那么就能得到**下单率**，感兴趣的小伙伴可以自己去做一下分析，看看双十二当天的数据星相比平时有什么变化？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bb3b72-574e-4ecd-a537-93c8f040ce81",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### ARPPU分析  \n",
    "\n",
    "ARPPU(average revenue per paying user)是指从每位付费用户中获得的收益，它反映的是整个平台的用户消费的均值。  \n",
    "\n",
    "它的计算方式为：ARPPU = 总收入/活跃用户付费数量。但是在该数据集中没有收益金额，因此我们可以对计算方式做一点转化，将总收入转化为总的购买行为次数。  \n",
    "\n",
    "定义如下：ARPPU = 当日总消费次数/当日活跃用户付费数量 ， 可以看出和ARPU唯一的区别是分母，ARPU的分母是**活跃用户数**（包含4种行为类型），ARPPU的分母是**活跃付费用户数**，因此ARPPU的计算会更简单："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47ec7014-e29f-44f6-9928-e99c3440e8ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>buy_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>54056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>79824</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>88930</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>247543</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>475826</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  user_id  buy_count\n",
       "0 2014-11-18    54056          1\n",
       "1 2014-11-18    79824          2\n",
       "2 2014-11-18    88930          2\n",
       "3 2014-11-18   247543          5\n",
       "4 2014-11-18   475826          3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_arppu = data[data['behavior_type']==4].groupby(['date','user_id'])['behavior_type'].count()\n",
    "data_user_arppu = data_user_arppu.reset_index().rename(columns={'behavior_type':'buy_count'})\n",
    "data_user_arppu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "261dda9b-dc8b-4b71-b931-5e227ab1dd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "arppu = data_user_arppu.groupby('date').apply(lambda x: x['buy_count'].sum() / x['user_id'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b0779c1-be29-4e75-8f0d-406892a00149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    2.423652\n",
       "2014-11-19    2.439444\n",
       "2014-11-20    2.320375\n",
       "2014-11-21    2.271429\n",
       "2014-11-22    2.530120\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arppu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "36c73ced-5cfc-4b33-818e-522ad476c41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'arppu')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arppu.plot()\n",
    "plt.title('arppu')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9f7b43-9972-4775-acc0-6458999bd0bd",
   "metadata": {},
   "source": [
    "可以看到，针对活跃的下单用户来讲，平均每日消费次数在2-2.5次之间波动，双十二当天该数值达超过3.75，一个可能的原因是用户会在平时把喜欢的商品进行加购，等到双十二促销当天再下单购买。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f504e8e6-0b05-4d78-8359-b269aec5d5df",
   "metadata": {},
   "source": [
    "### 复购情况分析  \n",
    "\n",
    "一般来说，复购是指对产品的重复购买行为。但是这个定义在商业上是不精确的，假若一个用户在一天内多次在淘宝购买商品，不能说明这件用户对淘宝的依赖（有可能是某位用户只是第一次用，但是买的量大）。因此商业分析过程中，对于复购行为进行明确的定义。这里的复购是指：两天以上都在该平台产生了购买行为，需要指出一天多次的购买不算是复购。  \n",
    "\n",
    "因此复购率的计算方式为：复购率 = 复购用户数量 / 有购买行为的用户数量。基于这个公式，我们先计算用户在数据集中的购买频次（即购买过商品的总人数）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "141a6ba2-7c12-4cc1-a24f-e0a5df1b359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 购买商品的总人数\n",
    "data_user_pay = data[data['behavior_type'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e462692-007c-43d0-a115-d896a17d2014",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对date去重，得到的结果即为用户在这一个内支付的天数：\n",
    "data_user_pay = data_user_pay.groupby('user_id')['date'].apply(lambda x:len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "482fdb49-d0cf-4b35-81e4-d7daf80ed3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算复购率\n",
    "repeat_buy_ratio = data_user_pay[data_user_pay > 1].count() / data_user_pay.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5800bcba-a99b-4ae4-8bd1-149a39f4d206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8717083051991897"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat_buy_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ffaff7-74cc-413c-a041-de046b1109a3",
   "metadata": {},
   "source": [
    "可以看到在这一个月中，用户的复购率还是非常高的，达到了87%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e686360-0084-41d0-b731-709360bd3b2c",
   "metadata": {},
   "source": [
    "### 复购周期分析  \n",
    "\n",
    "除了以上对复购频次的统计，还需要对复购意向做进一步的探究，想要知道用户多久复购一次。这个数据有助于淘宝产品宣传在这个时间间隔内采取策略，增加用户的复购意向，最终转化为实际收益。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "db7d3cb0-de1c-4d76-ab8c-9b8c4acaa97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user_repeat = data[data['behavior_type'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "61473531-7cde-4d1a-9d6e-080d76a4b3ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user_repeat = data_user_repeat.groupby(['user_id','date'])['item_id'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c64f496a-d9de-488d-8e36-08f7f7c2b930",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "225ba312-3001-478a-bf4c-5df06a5e19fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user_buy_date_diff = data_user_repeat.groupby('user_id')['date'].apply(lambda x:x.sort_values().diff(1).dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "772028f8-1eab-464d-9e8f-1099d4b207e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    \n",
       "4913     1     6 days\n",
       "         2     4 days\n",
       "         3     2 days\n",
       "         4     3 days\n",
       "7528     7     4 days\n",
       "         8     1 days\n",
       "         9     3 days\n",
       "         10    3 days\n",
       "         11   10 days\n",
       "7591     13    7 days\n",
       "Name: date, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy_date_diff.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad4e4ee-c8d3-4400-b1af-459eaa7b5cce",
   "metadata": {},
   "source": [
    "接下来我们对复购间隔进行可视化结果展示，看看有什么有价值的线索："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ae6bc761-e265-43fd-b131-751e8ccf907f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_user_buy_date_diff.value_counts().plot(kind='line')\n",
    "\n",
    "plt.xlabel('repeat_day_diff')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a3b4b6-4558-46e4-855e-323a6d5e1bc5",
   "metadata": {},
   "source": [
    "可以看出，大部分用户的复购行为发生在5天之内，在第5天复购的行为出现了明显的拐点，如果这个时候采取营销策略提升用户的购买意图，增加更多收益。超过15天后，复购的意愿基本已经趋于0，此时可以考虑采取一些召回策略，增加复购的可能性，防止用户的流失。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "595d68ae-11cf-4fbb-aa57-ed1fead8865d",
   "metadata": {},
   "source": [
    "## K-Means模型用户聚类分析  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "129b3a65-0158-4e82-a770-79714f774a82",
   "metadata": {},
   "source": [
    "### 特征选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8747b3fa-1a75-4b32-9e75-2bcf1b12cd12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# 计算访问频次\n",
    "visit_cnt = data.groupby(by=[\"user_id\"]).size()\n",
    "\n",
    "# 转化率\n",
    "trans_rate = (\n",
    "    data.groupby(by=[\"user_id\"])[\"behavior_type\"].agg(lambda x: x[x==4].size)\n",
    "    / visit_cnt\n",
    ")\n",
    "\n",
    "# 计算生命周期\n",
    "life_cycle = data.groupby(by=[\"user_id\"])[\"date\"].agg(lambda x: max(x) - min(x))\n",
    "\n",
    "# 计算RFM\n",
    "rfm = data.groupby(by=[\"user_id\"]).agg(\n",
    "    {\n",
    "        \"date\": lambda x: datetime(2014, 12, 18) - max(x),\n",
    "        \"behavior_type\": lambda x: x[x == 4].sum(),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "20daf98d-0048-4a48-b9f5-b15060c0ac2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>trans_rate</th>\n",
       "      <th>visit_cnt</th>\n",
       "      <th>life_cycle</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4913</td>\n",
       "      <td>0.003444</td>\n",
       "      <td>1742</td>\n",
       "      <td>30 days</td>\n",
       "      <td>0 days</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6118</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>117</td>\n",
       "      <td>29 days</td>\n",
       "      <td>0 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7528</td>\n",
       "      <td>0.028037</td>\n",
       "      <td>214</td>\n",
       "      <td>23 days</td>\n",
       "      <td>4 days</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7591</td>\n",
       "      <td>0.024447</td>\n",
       "      <td>859</td>\n",
       "      <td>30 days</td>\n",
       "      <td>0 days</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12645</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>268</td>\n",
       "      <td>30 days</td>\n",
       "      <td>0 days</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  trans_rate  visit_cnt life_cycle      R   F\n",
       "0     4913    0.003444       1742    30 days 0 days  24\n",
       "1     6118    0.008547        117    29 days 0 days   4\n",
       "2     7528    0.028037        214    23 days 4 days  24\n",
       "3     7591    0.024447        859    30 days 0 days  84\n",
       "4    12645    0.029851        268    30 days 0 days  32"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_feat = pd.DataFrame(\n",
    "    {\n",
    "        \"user_id\": visit_cnt.index,\n",
    "        \"trans_rate\": trans_rate.values,\n",
    "        \"visit_cnt\": visit_cnt.values,\n",
    "        \"life_cycle\": life_cycle.values,\n",
    "        \"R\": rfm[\"date\"].values,\n",
    "        \"F\": rfm[\"behavior_type\"].values,\n",
    "    }\n",
    ")\n",
    "user_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d32fb334-57ec-4c45-b170-c8c67064fc60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                 int64\n",
       "trans_rate            float64\n",
       "visit_cnt               int64\n",
       "life_cycle    timedelta64[ns]\n",
       "R             timedelta64[ns]\n",
       "F                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_feat.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ab358e3-c0b4-42b3-b7e5-ccce66ba553d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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